Abstract:
Natural Language Processing has been developed
to allow human-machine communication to take
place in a natural-language. Word Sense
Disambiguation (WSD) is regarded as one of the
most interesting and longest-standing problems in
NLP. Several methodological issues come up with
the context of WSD. These are supervised vs.
unsupervised WSD approaches. Supervised WSD
approaches have obtained better results than
unsupervised WSD approaches. Naïve Bayesian
WSD approach is one of the best supervised WSD
approaches. This paper presents a corpus-based
approach that uses Naïve Bayesian Classification to
disambiguate ambiguous words with part-of-speech
‘noun’, which uses topical feature that represent cooccurring
words in bag-of-word feature. This system
also uses Senseval-3 corpus as a training data for
Naïve Bayesian Classification, and access Word Net
for retrieving meaning of the resulted senses. This
system tokenizes and tags part-of-speech to each
word of input sentence, collect target words,
disambiguate target words and output correct sense
and meaning for each target word.
